Fandi Gou

Fandi Gou

Shanghai Jiao Tong University

Publications 📖

Accepted

  1. Fandi Gou, Zhengyu Guo, Hengyuan Zhao and Yunze Cai, Communication Limited Formation Control with Collision Avoidance via Knowledge Guided Reinforcement Learning. Neurocomputing, p. 135179, Sep 2026.
    • See a brief work description here.
  2. Fandi Gou, Chenyu Zhao, Hengyuan Zhao and Yunze Cai, Primal-Dual based Safe Reinforcement Learning for Multi-Agent Navigation with Graph Information Aggregation. Submitted to International Federation of Automatic Control World Congress 2026 (IFAC), Apr 2026.
    • See a brief work description here.
  3. Fandi Gou, Haikuo Du and Yunze Cai, A Graph-Based Safe Reinforcement Learning Method for Multi-agent Cooperation, Neural Networks (NN), vol. 199, p. 108693, Feb 2026.
    • See a brief work description here.
  4. Fandi Gou, Chenyu Zhao, Haikuo Du and Yunze Cai, Modeling Deception in Multi-Robot Target-Attacker-Defender Game via Deep Reinforcement Learning, in Proc IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Jun 2025.
    • See brief work description here.
  5. Fandi Gou, Haikuo Du, Chenyu Zhao and Yunze Cai, A Policy-Guided Reinforcement Learning Method for Encirclement Control in Multiobstacle Environment, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 36, no. 9, pp. 17034-17046, May 2025.
    • See brief work description here.

Under Review

  1. Fandi Gou, Chenyu Zhao, Hengyuan Zhao and Yunze Cai, Guide Policy Assisted Reinforcement Learning for Multi-agent Tasks under Restricted Communication. Submitted to Engineering Application of Artificial Intelligence (EAAI), Jun 2026, under review.
    • See a brief work description here.

  2. Hao Zhang, Fandi Gou, Haowen Wu, Hengyuan Zhao and Yunze Cai, Solving Three-Dimensional Target Attacker Defender Game via Deep Reinforcement Learning. Submitted to Chinese Journal of Aeronautics, Mar 2026, major revision.

  3. Hengyuan Zhao, Yun Zhang, Fandi Gou, Lixiu Yao and Yunze Cai, Online Incentive Strategy Learning for Two-Player Reverse Stackelberg Games via Persistent Inverse Reinforcement Learning. Submitted to IEEE Transactions on Automation Science and Engineering, Aug 2026, major revision.

In Preparation

In a period of steady accumulation. Immersed in the mathematics behind RL, curriculum learning theories in RL, and safety control these days. Putting paper writing on hold for now.